Variational methods, numerical PDE and PDE on graphs, convex optimization, mathematical imaging, data science, machine learning, and minimal surfaces. Mathematical problems at the cutting-edge frontier of image and data understanding, typically formulated as variational models, and the development of efficient algorithms for the numerical solution of these models and related PDE. Publications 20+ peer reviewed research papers published in journals and conference proceedings. See separate full list of publications.
[eng] Image processing problems have emerged as essential in our society. Indeed, in a world where ...
This work presents a method for segmenting images based on gradients in the intensity function. Past...
Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the...
In this article, we intend to give a broad picture of mathematical image processing through one of t...
This paper arose from a minisymposium held in 2018 at the 9th International Conference on Curves and...
This dissertation addresses general optimization in the field of computer vision. In this manuscript...
This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision a...
Energy minimization has become one of the most important paradigms for formulating image processing ...
Image segmentation is a central topic in image processing and computer vision and a key issue in man...
We analyze a variational approach to image segmentation that is based on a strictly convex non-quadr...
The text covers the theory of the Mumford and Shah model for digital image segmentation. The strong ...
We present a novel statistical and variational approach to image segmentation based on a new algorit...
The research in this dissertation has focused upon image segmentation and its related areas, using t...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
We present several graph-based algorithms for image processing and classification of high- dimension...
[eng] Image processing problems have emerged as essential in our society. Indeed, in a world where ...
This work presents a method for segmenting images based on gradients in the intensity function. Past...
Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the...
In this article, we intend to give a broad picture of mathematical image processing through one of t...
This paper arose from a minisymposium held in 2018 at the 9th International Conference on Curves and...
This dissertation addresses general optimization in the field of computer vision. In this manuscript...
This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision a...
Energy minimization has become one of the most important paradigms for formulating image processing ...
Image segmentation is a central topic in image processing and computer vision and a key issue in man...
We analyze a variational approach to image segmentation that is based on a strictly convex non-quadr...
The text covers the theory of the Mumford and Shah model for digital image segmentation. The strong ...
We present a novel statistical and variational approach to image segmentation based on a new algorit...
The research in this dissertation has focused upon image segmentation and its related areas, using t...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
We present several graph-based algorithms for image processing and classification of high- dimension...
[eng] Image processing problems have emerged as essential in our society. Indeed, in a world where ...
This work presents a method for segmenting images based on gradients in the intensity function. Past...
Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the...